# Super-resolution imaging based on reversible labeling

> **NIH NIH R01** · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · 2020 · $335,587

## Abstract

Abstract
The proposed research focuses on development of super-resolution optical imaging methods
that utilize reversible and transiently binding probes as the labeling reagents. Using of
reversibly-binding probes has the potential of achieving significantly higher labeling density and
enhanced spatial resolution. However, currently their application is limited by the lack of suitable
labeling reagents towards important cellular targets. Here we propose to expand the repertoire
of diffusive labeling tools by developing new labeling methods to facilitate broader usages of
PAINT methodology in biological studies and new statistical algorithms to achieve the optimal
spatial resolution from localization dataset collected with diffusive labeling. Specifically we will
focus on (1) developing imaging probes for phosphotyrosine signaling proteins based on Src-
homology 2 (SH2) domains, (2) developing a hybrid genetic-chemical labeling method based on
protein-peptide interactions that allows imaging of cellular proteins, and (3) developing statistical
models and numerical algorithms to achieve resolution enhancement when target molecules are
labeled in multiple rounds with reversible probes.

## Key facts

- **NIH application ID:** 9857036
- **Project number:** 5R01GM123784-04
- **Recipient organization:** UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
- **Principal Investigator:** Ji Yu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $335,587
- **Award type:** 5
- **Project period:** 2017-01-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9857036

## Citation

> US National Institutes of Health, RePORTER application 9857036, Super-resolution imaging based on reversible labeling (5R01GM123784-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9857036. Licensed CC0.

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